Project Summary This project proposes implementation of a computational discovery platform that quantifiably identifies and prioritizes both targets and compounds for therapeutic intervention. This platform is based on a novel fusion of two state of the art computational approaches (phenolog mapping and functional networks) with proprietary data from our powerful gene-drug screening assays (HIP, HOP, and YANA) that identify drug targets, conserved drug target pathways, and off-target effects (e.g. toxicity) by genome-wide phenotypic profiling. The extensive phenotypic profiles from these assays increases the power and scope of our phenolog algorithm, which maps orthologous phenotypes between species composed of conserved and functionally linked genes to define conserved biological modules, groups of gene that work tightly together. The functional network algorithm will quantify the association of these conserved modules with their disease phenotypes and drug responses. The first aim is to identify conserved gene modules that include disease genes in humans and genes with similar responses to a drug in yeast. To identify more potential disease genes and therapeutic compounds, conserved modules will be expanded using functional networks, which integrate large data sets to identify genes that work together. The broad applicability of the platform to human disease will be demonstrated by expanding the conserved modules to cover at least 70% of known human disease genes and 80% of the human proteome. The second aim is to quantitatively match pharmaceutical compounds to diseases that are likely to treat effectively using these expanded modules. In the first step, every conserved module will be scored for its potential role in every human disease. In the second step, every screened drug will be scored for it impact on each conserved module. The final step combines the two previous scores to match drugs to diseases. Correct identification of the targeted diseases of FDA approved drugs in multiple FDA therapeutic areas (e.g., cardiovascular, muscular dystrophy) will demonstrate the platform's ability to correctly predict a compound's potential as a new therapeutic intervention. This automated system to identify and prioritize therapeutic interventions across multiple diseases will increase the success rate of drug discovery and provide guidance to repurpose existing drugs for new indications. Implementation of the platform described in this proposal will strengthen Genetic Networks' contributions to the goal of bringing new treatments to patients faster.